Systems and methods for wafer bond monitoring

ABSTRACT

Systems and methods are provided for monitoring wafer bonding and for detecting or determining defects in a wafer bond formed between two semiconductor wafers. A wafer bonding system includes a camera configured to monitor bonding between two semiconductor wafers. Wafer bonding defect detection circuitry receives video data from the camera, and detects a bonding defect based on the received video data.

BACKGROUND

Wafer bonding, such as fusion wafer bonding, is a process in whichsemiconductor wafers may be bonded to each other. Typically, a qualityor state of the bonding can be detected only after the bonding has beencompleted, and in some cases, only after bonding of an entire batch ofwafers has been performed. Thus, in a case where one or more bondingdefects are present due to process conditions, for example, a pressurewithin the wafer bonding chamber, pressure applied to bond the wafers,pre-bonding processes, or the like, the bonding defects may not bedetermined until after many wafers have been bonded. This may result inexpenditure of significant time and cost to mitigate the damage causedby the improper bonding or the bonding defects, and in some cases, thismay result in scrapping of the improperly bonded wafers.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Aspects of the present disclosure are best understood from the followingdetailed description when read with the accompanying figures. It isnoted that, in accordance with the standard practice in the industry,various features are not drawn to scale. In fact, the dimensions of thevarious features may be arbitrarily increased or reduced for clarity ofdiscussion.

FIG. 1 is a block diagram schematically illustrating a wafer bondingapparatus, in accordance with some embodiments.

FIG. 2A is a diagram schematically illustrating a transparent-typeconfiguration of a wafer bonding apparatus, in accordance with someembodiments.

FIG. 2B is a diagram schematically illustrating a reflective-typeconfiguration of a wafer bonding apparatus, in accordance with someembodiments.

FIGS. 3A to 3C are views schematically illustrating different types ofsemiconductor wafers which may be bonded to one another in the waferbonding apparatus shown in FIG. 1 , in accordance with some embodiments.

FIG. 4 is a diagram schematically illustrating propagation of a bondingwave during bonding of semiconductor wafers, in accordance with someembodiments.

FIG. 5 is a block diagram schematically illustrating a wafer bondingmonitoring system, in accordance with some embodiments.

FIG. 6 is a flowchart illustrating a semiconductor wafer bonding defectdetection method, in accordance with some embodiments.

DETAILED DESCRIPTION

The following disclosure provides many different embodiments, orexamples, for implementing different features of the provided subjectmatter. Specific examples of components and arrangements are describedbelow to simplify the present disclosure. These are, of course, merelyexamples and are not intended to be limiting. For example, the formationof a first feature over or on a second feature in the description thatfollows may include embodiments in which the first and second featuresare formed in direct contact, and may also include embodiments in whichadditional features may be formed between the first and second features,such that the first and second features may not be in direct contact. Inaddition, the present disclosure may repeat reference numerals and/orletters in the various examples. This repetition is for the purpose ofsimplicity and clarity and does not in itself dictate a relationshipbetween the various embodiments and/or configurations discussed.

Further, spatially relative terms, such as “beneath,” “below,” “lower,”“above,” “upper” and the like, may be used herein for ease ofdescription to describe one element or feature's relationship to anotherelement(s) or feature(s) as illustrated in the figures. The spatiallyrelative terms are intended to encompass different orientations of thedevice in use or operation in addition to the orientation depicted inthe figures. The apparatus may be otherwise oriented (rotated 90 degreesor at other orientations) and the spatially relative descriptors usedherein may likewise be interpreted accordingly.

In various embodiments, the present disclosure provides systems,apparatuses, and methods in which wafer bonding may be monitored, inreal-time, to observe bonding wave propagation as the wafers are bondedto one another.

Embodiments provided herein include systems and methods for determiningthe presence of bonding defects during bonding of semiconductor wafersbased on video data that is acquired of the bonding of the wafers. Insome embodiments, machine learning techniques are utilized to analyzethe acquired video data, and the analysis may be based at least in parton historical or past video data of bonding of semiconductor wafers thatis stored in a bonding defect database.

FIG. 1 is a block diagram schematically illustrating a wafer bondingapparatus 100, in accordance with one or more embodiments of the presentdisclosure.

As shown in FIG. 1 , the wafer bonding apparatus 100 includes a waferbonding chamber in which a first wafer chuck 12 and a second wafer chuck14 are positioned opposite one another, for example, facing each other.In various embodiments, the first wafer chuck 12 and the second waferchuck 14 may be used to bond a first semiconductor wafer 22 to a secondsemiconductor wafer 24. The wafer bonding chamber 10 may be a chamberwithin which bonding of the first and second semiconductor wafers 22, 24is performed.

The first and second wafer chucks 12, 14 may be configured torespectively hold or carry the first and second semiconductor wafers 22,24 so that the semiconductor wafers 22, 24 may be brought into contactand bonded with one another in the wafer bonding apparatus 100. Invarious embodiments, the first and second wafer chucks 12, 14 mayinclude any features or functionalities of known wafer chucks forsemiconductor wafer bonding. In some embodiments, the first and secondwafer chucks 12, 14 may have one or more openings (for example, at acontact surface at which the wafer chucks 12, 14 contact thesemiconductor wafers 22, 24) through which a vacuum pressure may beapplied to hold the semiconductor wafers 22, 24 to the first and secondwafer chucks 12, 14 by suction. In other embodiments, the first andsecond semiconductor wafers 22, 24 may be respectively held by the firstand second wafer chucks 12, 14 by mechanical interaction, such as by oneor more clamps, retaining rings, or the like which may apply amechanical holding force to the semiconductor wafers 22, 24, therebyholding the semiconductor wafers 22, 24 in a desired position by thefirst and second wafer chucks 12, 14.

In various embodiments, the first and second wafer chucks 12, 14 mayinclude silicon based materials, such as glass, silicon oxide, siliconnitride, or other materials, such as aluminum oxide, combinations of anyof these materials, or the like. The first wafer chuck 12 may have adiameter that is suitable to hold the first semiconductor wafer 22, andthe second wafer chuck 14 may have a diameter that is suitable to holdthe second semiconductor wafer 14. The diameters of the first and secondwafer chucks 12, 14 may be selected, and may vary, based on a diameterof the first and second semiconductor wafers 22, 24, respectively. Insome embodiments, the diameters of the first and second wafer chucks 12,14 may be the same or substantially the same, while in otherembodiments, the first and second wafer chucks 12, 14 may have differentdiameters, sizes, or the like. For example, where the first and secondsemiconductor wafers 22, 24 have different sizes (e.g., differentdiameters), the first and second wafer chucks 12, 14 may similarly havedifferent sizes (e.g., different diameters) to accommodate the first andsecond wafers 22, 24.

During performance of wafer bonding by the wafer bonding apparatus 100,the first and second semiconductor wafers 22, 24 are brought intocontact with one another, for example, by motion of the first and secondwafer chucks 12, 14 toward one another, as illustrated by the doublearrow 30. In various embodiments, one of the first and second waferchucks 12, 14 may be movable with respect to the other of the first andsecond wafer chucks 12, 14, and in some embodiments, both of the firstand second wafer chucks 12, 14 are movable toward one another.

In some embodiments, the wafer bonding apparatus 100 includes acontroller or control circuitry 32 that controls one or more operationsof the wafer bonding apparatus 100. For example, in various embodiments,the controller 32 may control forces or pressures applied to thesemiconductor wafers 22, 24 during bonding, e.g., by control of thefirst and second wafer chucks 12, 14 to bring them toward one anotherand to press the first and second semiconductor wafers 22, 24 togetherduring bonding. In various embodiments, the controller 32 may controlother wafer bonding parameters, such as a pressure within the waferbonding chamber 10, a vacuum pressure or mechanical pressure applied bythe first and second wafer chucks 12, 14 to hold the first and secondsemiconductor wafers 22, 24, heating or other environmental parametersof the wafer bonding chamber 10, or the like.

The controller 32 may include a processor 34 and a computer-readablememory 36. The memory 36 may include machine readable instructions thatwhen executed by the processor 34, cause the controller 32 to sendcommand signals to one or more components of the wafer bonding apparatus100, such as the first and second wafer chucks 12, 14, vacuum orpressure mechanisms, heating mechanisms, or the like. In someembodiments, the controller 32 may control operations of the lightsources 16 a, 16 b and the cameras 18 a, 18 b. For example, thecontroller 32 may control emission of light by the light sources 16 a,16 b, and may control imaging by the cameras 18 a, 18 b, for example, byturning on and off the light sources 16 a, 16 b and the cameras 18 a, 18b.

The wafer bonding apparatus 100 further includes one or more lightsources 16 a, 16 b and one or more cameras 18 a, 18 b. The one or morecameras 18 a, 18 b are positioned to monitor the bonding of the firstand second semiconductor wafers 22, 24 in the wafer bonding apparatus100. The cameras 18 a, 18 b may be video cameras which acquire real-timevideo data of the bonding process as the bonding process occurs. In someembodiments, the cameras 18 a, 18 b may be infrared (IR) video camerasconfigured to acquire video images using light in the infrared spectrum(which may include, for example, light in the near-infrared,mid-infrared, and far-infrared wavelengths). However, embodiments of thepresent disclosure are not limited thereto, and in various embodiments,the cameras 18 a, 18 b may be configured to acquire video images usinglight of any wavelengths, including, for example, light within thevisible spectrum, ultraviolet spectrum, or any other wavelengths oflight.

In some embodiments, the one or more light sources 16 a, 16 b emit lighthaving wavelengths within a range from about 0.7 μm to about 1 μm, whichincludes Near-Infrared, Mid-Infrared, and Far-Infrared wavelengths. Insome embodiments, the light sources 16 a, 16 b emit white light (orvisible light) as well as light in the IR spectrum.

The one or more light sources 16 a, 16 b emit light toward thesemiconductor wafers 22, 24 during bonding of the semiconductor wafers22, 24 in the wafer bonding apparatus 100. In some embodiments, thelight emitted by the light sources 16 a, 16 b may be transmitted throughthe first and second semiconductor wafers 22, 24 and received by one ormore cameras 18 a, 18 b, and in some embodiments, the light emitted bythe light sources 16 a, 16 b may be reflected by the first or secondsemiconductor wafers 22, 24 and received by one or more cameras 18 a, 18b. This will be described in further detail with reference to FIGS. 2Aand 2B. It will be readily appreciated that while FIG. 1 illustrates twolight sources 16 a, 16 b and two cameras 18 a, 18 b, in variousembodiments, more or fewer than two light sources and cameras may beincluded in the wafer bonding apparatus 100. For example, in someembodiments, a single light source 16 a is configured to transmit lightthrough the semiconductor wafers 22, 24, and the light may be receivedby a single camera 18 a. In other embodiments, a single light source 16b may be configured to transmit light that is reflected by one or bothof the semiconductor wafers 22, 24 and the reflected light is receivedby a single camera 18 b.

In some embodiments, the first and second wafer chucks 12, 14 are atleast partially transparent to the light emitted by the light sources 16a, 16 b so that light may be transmitted through the wafer chucks 12, 14and at least partially into, and in some embodiments through, the firstand second semiconductor wafers 22, 24.

FIG. 2A schematically illustrates a transparent-type configuration ofthe wafer bonding apparatus 100 in which the first and secondsemiconductor wafers 22, 24 are positioned between the light source 16 aand the camera 18 a during bonding of the first and second semiconductorwafers 22, 24. The light source 16 a is positioned at one side of thewafers 22, 24 and is configured to transmit light through both the firstand second semiconductor wafers 22, 24, and the camera 18 a ispositioned at the opposite side of the wafers 22, 24 and is configuredto receive the light after it passes through both the first and secondsemiconductor wafers 22, 24. In this way, the camera 18 a may monitor abonding wave as the first and second semiconductor wafers 22, 24 arebonded together, through imaging of the first and second semiconductorwafers 22, 24 during bonding while the light (e.g., IR light) istransmitted through the first and second semiconductor wafers 22, 24.

As shown in FIG. 2A, the light emitted by the light source 16 a may havean incidence angle θ at the bonding interface between the first andsecond semiconductor wafers 22, 24, measured with respect to a plane ofthe bonding interface (e.g., measured with respect to the horizontaldirection, as shown). The incidence angle θ may be any suitable anglefor transmission of the light through the first and second semiconductorwafers 22, 24. In some embodiments, the incidence angle θ is within arange from 90° to 180°. In some embodiments, the camera 18 a ispositioned directly across from the light source 16 a, e.g., with thecamera 18 a aligned with an emission axis along which the light emittedfrom the light source 16 a travels; however, embodiments of the presentdisclosure are not limited thereto, and in some embodiments, the camera18 a may be positioned such that it is not aligned with the emissionaxis of the light source 16 a.

FIG. 2B schematically illustrates a reflective-type configuration of thewafer bonding apparatus 100 in which the light source 16 b and thecamera 18 b are positioned on a same side with respect to the first andsecond semiconductor wafers 22, 24 (e.g., above or below the wafers)during bonding of the first and second semiconductor wafers 22, 24. Thelight source 16 b is positioned at one side of the wafers 22, 24 and isconfigured to transmit light toward the first semiconductor wafer 22.The light transmitted by the light source 16 b is reflected by one orboth of the first and second semiconductor wafers 22, 24 and is receivedby the camera 18 b which is positioned at the same side of the wafers22, 24 as the light source 16 b. The light may be reflected by anysurface of the first or second semiconductor wafers 22, 24, and in someembodiments, the light is reflected at or near the interface at whichthe first and second semiconductor wafers 22, 24 are bonded to oneanother.

In this way, the camera 18 b may monitor a bonding wave as the first andsecond semiconductor wafers 22, 24 are bonded together, through imagingof the first and second semiconductor wafers 22, 24 during bonding whilethe light (e.g., IR light) is transmitted at least partially into thefirst semiconductor wafer 22, 24 and is reflected at or near the bondinginterface of the first and second semiconductor wafers 22, 24 and isreceived by the camera 18 b.

As shown in FIG. 2B, the light emitted by the light source 16 b may havean incidence angle θ at the bonding interface between the first andsecond semiconductor wafers 22, 24, measured with respect to a plane ofthe bonding interface (e.g., measured with respect to the horizontaldirection, as shown). The incidence angle θ may be any suitable anglefor transmission of the light at least partially into the firstsemiconductor wafer 22 and reflection of the light at or near thebonding interface of the first and second semiconductor wafers 22, 24.In some embodiments, the incidence angle θ is within a range from 0° to90°, and in some embodiments, the incidence angle θ is within a rangefrom 30° to 60°. In some embodiments, the camera 18 b is positioned atan angle with respect to the plane of the bonding interface that is thesame or substantially the same as the incidence angle θ; however,embodiments of the present disclosure are not limited thereto, and insome embodiments, the camera 18 b may be positioned at any angle withrespect to the bonding interface suitable for receiving the reflectedlight.

While the first and second wafer chucks 12, 14 are not shown in thewafer bonding apparatus 100 shown in FIGS. 2A and 2B, it will be readilyappreciated that the first and second wafer chucks 12, 14, as well asany other components, features or the like of wafer bonding apparatuses,tools or systems which may be known to those skilled in the art, may beincluded in the apparatus 100. The first and second wafer chucks 12, 14,in some embodiments, may be at least partially transparent so that thelight emitted by the light source 16 a may pass through one or both ofthe first and second wafer chucks 12, 14, and in other embodiments, oneor both of the first and second wafer chucks 12, 14 may have openingsthrough which the light transmitted by the light source 16 a, 16 bpasses (e.g., the first and second wafer chucks 12, 14 may hold thewafers 22, 24 at their edges so that the wafers 22, 24 are exposed tothe light emitted by the light source 16 a, 16 b through the openings inthe first and second wafer chucks 12, 14).

Referring again to FIG. 1 , the wafer bonding apparatus 100 may includeany number of light sources 16 a, 16 b and any number of cameras 18 a,18 b. For example, in some embodiments, the wafer bonding apparatus 100may have a configuration that is a combination of the transparent-typeand the reflective-type, e.g., with a first camera 18 a positionedopposite a first light source 16 a and configured to receive light thatis transmitted through the first and second semiconductor wafers 22, 24,and a second camera 18 b positioned on a same side as a second lightsource 16 b and configured to receive light that is reflected by thefirst and second semiconductor wafers 22, 24.

In some embodiments, the wafer bonding apparatus 100 may include aplurality of cameras 18 a, 18 b, each of which may be positioned at adifferent position, orientation, or angle with respect to thesemiconductor wafers being bonded. In some embodiments, using multiplecameras, the wafer bonding apparatus 100 may construct a 3D image of thebonding of the wafers.

The wafer bonding apparatus 100 may be configured to monitor bondingbetween semiconductor wafers having a plurality of different materials,e.g., at the interface between the first and second semiconductor wafers22, 24. For example, in various embodiments, the wafer bonding apparatus100 may be configured to monitor bonding of semiconductor wafers havingas a bonding surface any of a thermal oxidation layer, a poly-siliconlayer, silicon nitride, silicon oxide, or any other material. Moreover,the wafers being bonded to one another (e.g., the first and secondsemiconductor wafers 22, 24) may be semiconductor wafers that are at anystage of processing. For example, in various embodiments, thesemiconductor wafers may be unpatterned wafers (which may be referred toherein as “dummy wafers”), which may be semiconductor wafers that havenot yet been patterned or otherwise processed to form, for example,electrical features such as metal or conductive layers, polysiliconlayers, or the like. In some embodiments, the unpatterned or dummywafers may include an unpatterned material at the bonding surface, suchas an unpatterned layer of an oxide or the like. In some embodiments,the semiconductor wafers may be patterned wafers, which may besemiconductor wafers which have been patterned or otherwise processed toform any of a variety of electrical features, including for example,patterned metal or conductive layers, polysilicon layers, or the like.

The semiconductor wafers that are bonded to one another in the waferbonding apparatus 100, such as the first and second semiconductor wafers22, 24, may be wafers of any semiconductor material. In variousembodiments, the first and second semiconductor wafers 22, 24 may bemonocrystalline silicon (Si) wafers, amorphous Si wafers, galliumarsenide (GaAs) wafers, or any other semiconductor wafers.

FIGS. 3A to 3C are views schematically illustrating different types ofsemiconductor wafers which may be bonded to one another, for example, inthe wafer bonding apparatus 100 shown in FIG. 1 .

FIG. 3A illustrates an example in which a first semiconductor wafer 122is bonded to a second semiconductor wafer 124, for example, by the waferbonding apparatus 100. In the example of FIG. 3A, both the first andsecond semiconductor wafers 122, 124 are dummy wafers. The wafer bondingapparatus 100 may be utilized to bond the first and second semiconductorwafers 122, 124 to one another, and the bonding interface between thefirst and second semiconductor wafers 122, 124 may be free of patternedfeatures, as both of the first and second semiconductor wafers 122, 124are dummy wafers. The bonding surfaces of the first and secondsemiconductor wafers 122, 124 may be, in various embodiments, a thermaloxidation layer, silicon nitride, silicon oxide, or any otherunpatterned material.

FIG. 3B illustrates an example in which a first semiconductor wafer 222is bonded to a second semiconductor wafer 224, for example, by the waferbonding apparatus 100. In the example of FIG. 3B, the firstsemiconductor wafer 222 is a patterned wafer, while the secondsemiconductor wafer 224 is a dummy wafer. The first semiconductor wafer222 may include a plurality of patterned features 223 at the bondingsurface, e.g., the surface which is bonded to the second semiconductorwafer 222. The patterned features 223 may be, for example, patternedelectrical features, including for example, patterned metal orconductive layers, polysilicon layers, or the like.

The wafer bonding apparatus 100 may be utilized to bond the first andsecond semiconductor wafers 222, 224 to one another, and the bondinginterface between the first and second semiconductor wafers 222, 224include patterned features at one side (e.g., at the bonding surface ofthe first semiconductor wafer 222) and may be free of patterned featuresat the other side (e.g., at the bonding surface of the secondsemiconductor wafer 224). The bonding surface of the secondsemiconductor wafer 224 may be, in various embodiments, a thermaloxidation layer, silicon nitride, silicon oxide, or any otherunpatterned material.

While the patterned wafer (i.e., the first semiconductor wafer 222) isshown in FIG. 3B as being disposed over the dummy wafer (i.e., thesecond semiconductor wafer 224), it will be readily appreciated that invarious embodiments the patterned wafer may be disposed below the dummywafer.

FIG. 3C illustrates an example in which two patterned semiconductorwafers, e.g., a first semiconductor wafer 322 and a second semiconductorwafer 324, are bonded to one another, for example, by the wafer bondingapparatus 100. In the example of FIG. 3C, both the first and secondsemiconductor wafers 322, 324 are patterned wafers, each of which mayinclude, for example, a plurality of patterned features 323 at thebonding surface, e.g., the surfaces of the first and secondsemiconductor wafers 322, 324 that are bonded together. The patternedfeatures 323 may be, for example, patterned electrical features,including for example, patterned metal or conductive layers, polysiliconlayers, or the like.

The wafer bonding apparatus 100 shown in FIG. 1 monitors the bonding ofthe first and second semiconductor wafers 22, 24, which may be dummywafers, patterned wafers, or one dummy wafer and one patterned wafer, asshown and described in the examples of FIGS. 3A to 3C. During bonding ofthe semiconductor wafers 22, 24, the one or more cameras 18 a, 18 bmonitor the bonding state of the wafers, such as by monitoringpropagation of a bonding wave and monitoring unbonded regions or defectregions during the bonding.

FIG. 4 schematically illustrates propagation of a bonding wave duringbonding of first and second semiconductor wafers, for example, by thewafer bonding apparatus 100. As shown at 401, bonding begins at a bondstart point 412, which may be a point at which the first and secondsemiconductor wafers 22, 24 first contact one another and form a bondbetween each other. The bond start point 412 may occur at variouspositions of the first and second semiconductor wafers 22, 24, dependingupon a variety of factors, such as warpage of one or both of thesemiconductor wafers 22, 24, the structure of the semiconductor wafers22, 24 (e.g., dummy or patterned), or various other factors.

At 402, the bond wave 414 expands outwardly from the start point 412.The bond wave 414 may expand outwardly in all directions from the startpoint 412 so that the bonded region between the first and secondsemiconductor wafers 22, 24 grows as the bonding continues. When thebond wave 414 reaches an edge of the semiconductor wafers 22, 24, thebond wave 414 may continue to expand about the edge of the wafers.

At 403, the bond wave 414 continues expanding across the first andsecond semiconductor wafers 22, 24. At 404, the bond wave 414 reachesall edges of the first and second semiconductor wafers 22, 24. However,one or more unbonded regions 416 may exist after the bonding process iscompleted. The unbonded region 416 represents a region at the interfaceof the first and second semiconductor wafers 22, 24 where the wafers didnot properly bond to one another. The unbonded region 416 may be causedby any of a variety of factors, including, for example, wafer warpage sothat the wafers do not properly contact one another during bonding, thepresence of particles or other contaminants at the surface of one of thewafers that prevents the wafers from fully bonding, conditions in thebonding chamber during bonding (e.g., the bonding pressure applied tobond the wafers together being too high or too low), non-uniform filmdeposition on the bonding surface of one of the wafers, or the like.

The wafer bonding apparatus 100 may be used to monitor the propagationof the bond wave 414 while the first and second semiconductor wafers 22,24 are bonded to one another. For example, by irradiating the first andsecond semiconductor wafers 22, 24 with light (e.g., IR light) emittedby the one or more light sources 16 a, 16 b and imaging the bondinginterface between the first and second semiconductor wafers 22, 24 bythe one or more cameras 18 a, 18 b (e.g., video cameras), the bond wave414 may be monitored from the initial or start point at which the wafersfirst contact one another and begin to bond until the bonding process iscompleted. The video information acquired by the cameras 18 a, 18 bindicate bonding quality based on a brightness of the infrared (IR)light that is emitted by the light sources 16 a, 16 b and detected bythe cameras 18 a, 18 b. For example, the acquired video data mayindicate bonded regions as regions at which the IR light is brighter inthe acquired video data, while unbonded regions or bonding defects maybe indicated by darker regions in the video data.

FIG. 5 is a block diagram schematically illustrating a wafer bondingmonitoring system 500, in accordance with one or more embodiments. Thewafer bonding monitoring system 500 may be used in conjunction with, andmay include one or more of the features and functionality of, the waferbonding apparatus 100 shown in FIG. 1 . For example, the wafer bondingmonitoring system 500 may include the wafer bonding apparatus 100,including the cameras 18 a, 18 b and the controller 32. However,embodiments provided by the present disclosure are not limited thereto.

The cameras 18 a, 18 b acquire video data associated with bonding of thefirst and second semiconductor wafers 22, 24 during bonding in the waferbonding apparatus 100. The cameras 18 a, 18 b are communicativelycoupled to bonding defect recognition circuitry 510 so that the bondingdefect detection circuitry 510 receives video data associated withwafers being bonded that is output by the cameras 18 a, 18 b. In someembodiments, the video data that is acquired by the cameras 18 a, 18 bis provided in real-time to the bonding defect detection circuitry 510so that the bonding defect detection circuitry 510 may detect ordetermine the presence of defects in the bonding of the first and secondsemiconductor wafers 22, 24 in real-time while the bonding occurs. Insome embodiments, the bonding defect detection circuitry 510 may detector determine the presence of defects in the bonding of the first andsecond semiconductor wafers 22, 24 within 2 seconds of a time the videodata is acquired by the cameras 18 a, 18 b. In some embodiments, thebonding defect detection circuitry 510 may detect or determine thepresence of defects in the bonding of the first and second semiconductorwafers 22, 24 within 1 second or less of a time the video data isacquired by the cameras 18 a, 18 b.

The cameras 18 a, 18 b may be communicatively coupled to the bondingdefect detection circuitry 510 by any suitable communications network501. The communications network 501 may utilize one or more protocols tocommunicate via one or more physical networks, including local areanetworks, wireless networks, dedicated lines, intranets, the Internet,and the like.

In some embodiments, the communications network 501 includes one or moreelectrical wires which communicatively couple the cameras 18 a, 18 b tothe bonding defect detection circuitry 510. In some embodiments, thecommunications network 501 may include a wireless communications networkfor communicating signals from the cameras 18 a, 18 b to the bondingdefect detection circuitry 510. In some embodiments, the bonding defectdetection circuitry 510 may be included as part of the controller 32,such as part of the processor 34, of the wafer bonding apparatus 100shown in FIG. 1 .

The bonding defect detection circuitry 510 may be or include anyelectrical circuitry configured to perform the bonding defect detectiontechniques described herein. In some embodiments, the bonding defectdetection circuitry 510 may include or be executed by a computerprocessor, a microprocessor, a microcontroller, or the like, configuredto perform the various functions and operations described herein withrespect to the bonding defect detection circuitry 510. For example, thebonding defect detection circuitry 510 may be executed by a computerprocessor selectively activated or reconfigured by a stored computerprogram, or may be a specially constructed computing platform forcarrying out the features and operations described herein. In someembodiments, the bonding defect detection circuitry 510 may beconfigured to execute software instructions stored in anycomputer-readable storage medium, including, for example, read-onlymemory (ROM), random access memory (RAM), flash memory, hard disk drive,optical storage device, magnetic storage device, electrically erasableprogrammable read-only memory (EEPROM), organic storage media, or thelike.

The bonding defect detection circuitry 510 may receive images (e.g.,video data, which may represent a sequence of acquired images in videoform) from the cameras 18 a, 18 b of semiconductor wafers that areundergoing bonding by the wafer bonding apparatus 100. The bondingdefect detection circuitry 510 analyzes the video data to detect ordetermine the presence of defects in the bonding of the semiconductorwafers, for example, based on a comparison of the received video data ofthe wafer bonding with past data or analysis of the received video databy a machine learning model that is trained with past data (e.g., pastvideo image data of bonding of semiconductor wafers in which the bondingis determined to have one or more defects) indicative of defects.

In some embodiments, the bonding defect detection circuitry 510 maydetect or determine the presence of defects in a wafer bond by employingone or more artificial intelligence or machine learning techniques,which in some embodiments may be implemented at least in part by machinelearning circuitry 520. Some or all of the detections or determinationsdescribed herein that are made by the bonding defect detection circuitry510 may be performed automatically by the bonding defect detectioncircuitry 510, for example, in response to receiving video data of awafer bond between semiconductor wafers that is being monitored by thecameras 18 a, 18 b. The machine learning circuitry 520 may be includedas part of the bonding defect detection circuitry 510 (as shown), or maybe remotely located and communicatively coupled to the bonding defectdetection circuitry 510. The machine learning circuitry 520 may detector determine the presence of defects in wafer bond by using past data(e.g., the machine learning circuitry 520 may be trained based on pastdata) indicative of defects in wafer bonding between semiconductorwafers, and the machine learning circuitry 520 may compare the receivedvideo data with the past data to detect or determine the presence ofdefects in a wafer bond based on similarities or deviations from thepast data or from a trained model contained within, managed by, orotherwise accessible to the machine learning circuitry 520.

“Artificial intelligence” is used herein to broadly describe anycomputationally intelligent systems and methods that can learn knowledge(e.g., based on training data), and use such learned knowledge to adaptits approaches for solving one or more problems, for example, by makinginferences based on a received input, such as the received video data orvideo images of semiconductor wafer bonding. Machine learning generallyrefers to a sub-field or category of artificial intelligence, and isused herein to broadly describe any algorithms, mathematical models,statistical models, or the like that are implemented in one or morecomputer systems or circuitry, such as processing circuitry, and whichbuild one or more models based on sample data (or training data) inorder to make predictions or decisions.

The bonding defect detection circuitry 510 or the machine learningcircuitry 520 may employ, for example, neural network, deep learning,convolutional neural network, Bayesian program learning, support vectormachines, computer vision, and pattern recognition techniques to solveproblems such as predicting or determining the presence of defects insemiconductor structure samples. Further, the bonding defect detectioncircuitry 510 or the machine learning circuitry 520 may implement anyone or combination of the following computational algorithms ortechniques: classification, regression, supervised learning,unsupervised learning, feature learning, clustering, decision trees,image recognition, or the like.

As one example, an artificial neural network may be utilized by thebonding defect detection circuitry 510 or the machine learning circuitry520 to develop, train, or update one or more machine learning modelswhich may be utilized to detect or determine the presence of defects inwafer bonds. An example artificial neural network may include aplurality of interconnected “neurons” which exchange information betweeneach other. The connections have numeric weights that can be tuned basedon experience, and thus neural networks are adaptive to inputs and arecapable of learning. The “neurons” may be included in a plurality ofseparate layers which are connected to one another, such as an inputlayer, a hidden layer, and an output layer. The neural network may betrained by providing training data (e.g., past data or past images whichare indicative of defects in semiconductor wafer bonds) to the inputlayer. Through training, the neural network may generate and/or modifythe hidden layer, which represents weighted connections mapping thetraining data provided at the input layer to known output information atthe output layer (e.g., classification of received video data as havingone or more defects, defective conditions, or the like). Relationshipsbetween neurons of the input layer, hidden layer, and output layer,formed through the training process and which may include weightconnection relationships, may be stored, for example, as one or moremachine learning models within or otherwise accessible to the machinelearning circuitry 520.

Once the neural network has been sufficiently trained, the neuralnetwork may be provided with non-training data (e.g., new video data orvideo images of wafer bonds formed between semiconductor wafers duringoperation of the wafer bonding apparatus 100) at the input layer.Utilizing bonding defect knowledge (e.g., as stored in the machinelearning model, and which may include, for example, weighted connectioninformation between neurons of the neural network), the neural networkmay make determinations about the received images at the output layer.For example, the neural network may detect or determine the presence ofdefects in the bonding between semiconductor wafers during performed ofwafer bonding by the wafer bonding apparatus 100.

Employing one or more computationally intelligent and/or machinelearning techniques, the bonding defect detection circuitry 510 maylearn (e.g., by developing and/or updating a machine learning algorithmor model based on training data) to detect or determine the presence ofdefects in bonding of semiconductor wafers based at least in part onknowledge, inferences or the like developed or otherwise learned throughtraining of the machine learning circuitry 520.

The machine learning circuitry 520 may be implemented in one or moreprocessors having access to instructions, which may be stored in anycomputer-readable storage medium, which may be executed by the machinelearning circuitry 520 to perform any of the operations or functionsdescribed herein.

In some embodiments, the machine learning circuitry 520 iscommunicatively coupled to a bonding video database 542, which may bestored, for example, in any computer-readable storage medium. Thebonding video database 542 may include video information associated withpast wafer bonding processes. For example, the bonding video database542 may store a plurality of video clips of bonding processes which havepreviously been performed to bond two wafers together. The video clipsstored in the bonding video database 542 may be utilized to compare withcurrent video data (e.g., real-time video data of a current bondingprocess of two wafers) to determine whether the current bonding processis normal or abnormal, and to detect or determine the presence ofbonding defects in the bonding of the wafers. The video clips stored inthe bonding video database 542 may be video clips of any temporallength. In some embodiments, the video clips may include video clips ofpast bonding processes having a length of more than 1 minute. Forexample, the video clips stored in the bonding video database 542 mayinclude video clips of entire wafer bonding processes betweensemiconductor wafers. In some embodiments, the video clips stored in thebonding video database 542 may include video clips of shorter durationthan that of an entire wafer bonding process, and may include, forexample, video clips having a temporal duration of less than 1 minute,less than 30 seconds, and in some embodiments, less than 5 seconds. Suchvideo clips may be used to train the machine learning circuitry 520 torecognize or detect defects based on short, medium, and long durationsof video clips of past data.

In some embodiments, the past video information stored in the bondingvideo database 542 may be utilized as training data to train the machinelearning circuitry 520 or the bonding defect detection circuitry 510.That is, the historical or past video information may be provided astraining data for training the machine learning circuitry 520, and thealgorithm or machine learning model contained within or accessible tothe machine learning circuitry 520 may be updated or modified based onthe past video information stored in the bonding video database 542, sothat the trained machine learning circuitry 520 may detect or determinethe presence of defects in semiconductor wafer bonds, and in someembodiments, may determine whether a current bonding process is normalor abnormal.

In some embodiments, the training data (e.g., the past video informationstored in the bonding video database 542) may be labeled training datafrom which the machine learning circuitry 520 and/or the bonding defectprediction circuitry 510 may learn to detect or determine the presenceof bonding defects during the bonding process, and in some embodiments,to determine an action to take in response to the detection of a bondingdefect. The labeled training data may indicate, for example, that aparticular video clip in the wafer bonding video data represents thepresence of a bonding defect, and in some embodiments, labeled trainingdata may indicate a normal bonding process. Training may be based on awide variety of learning algorithms or models, including, for example,support vector machines, linear regression, logistic regression, naiveBayes, linear discriminant analysis, decision trees, k-nearest neighbor,neural networks, or the like.

The training data (e.g., the past video information stored in thebonding video database 542) may include video information associatedwith a variety of different types of bonding defects. For example, thevideo information may include video information associated with bondingdefects due to the position of a bonding start point, as certain bondingprocesses may normally start at a particular position (e.g., near alower point or an outer region of the wafers being bonded). Abnormal ordefective bonding processes may be indicated by video data in which thebonding start point occurs at a position that is different from a normalbonding start point. Further, the video information may include videoinformation associated with bonding defects due to multiple bond startpoints occurring concurrently, such as when bonding begins at twodifferent points at the interface between the two wafers being bonded.This may be indicative of a bonding defect in certain bonding processes.Moreover, the video information may include video information associatedwith defects due to the presence of particles on one of the wafers,defects due to a non-uniform bonding wave propagation speed, defects dueto a void in the bonding between two wafers, defects due to a defectivepre-treatment or pre-clean process, or any other defects due to anyother cause.

The bonding wave propagation speed may be related to the strength of thewafer bond, and thus the bonding wave propagation speed may indicate thepresence of bonding defects, e.g., bonding defects due to a low strengthwafer bond. In some embodiments, the bond wave propagation speed may benon-uniform as the bond wave propagates across the surfaces (e.g., thebonding interface) of the semiconductor wafers undergoing the bonding.This non-uniform speed may be due to various defects of the wavers, suchas an irregular topography of the wafers (e.g., due to non-uniform filmdeposition, or the like), wafer warpage, or the like. Accordingly, thenon-uniform speed of the bond wave propagation may be utilized toindicate the presence of a bonding defect. Moreover, bonding defects maybe indicated by the presence of a bond void (e.g., an unbonded area),which may be regions at which bonding between the two semiconductorwafers is incomplete or very weak. The training data, e.g., the pastvideo information associated with bonding defects, may include dataassociated with a variety of different semiconductor wafers having avariety of different layout patterns, die counts, interface materials,pre-treatment layers or processes that are performed, or the like.Different types of the semiconductor wafers undergoing bonding may havedifferent bonding parameters, e.g., wafers having different layoutpatterns, die counts, interface materials, pre-treatment layers, or thelike may have different bond wave propagation speeds, bond wave startpoints, or the like. As such, training data provided for a variety ofdifferent cases may be utilized to indicate the presence of bondingdefects.

In some embodiments, the machine learning circuitry 520 implements apattern recognition algorithm to determine, in real time, whether amonitored wafer bonding process is normal or abnormal, and in someembodiments, the machine learning circuitry 520 may determine an actionto take in response to the bonding process being determined to beabnormal. In some embodiments, the machine learning circuitry 520 or thebonding defect detection circuitry 510 may implement one or more machinelearning or artificial intelligence techniques (e.g., by the machinelearning circuitry 520) to determine whether a monitored wafer bondingprocess is normal or abnormal, and an action to take in response to thebonding process being determined to be abnormal.

The machine learning circuitry 520 or the bonding defect detectioncircuitry 510 may be trained to recognize a plurality of different typesof bonding defects. For example, some bonding defects may be determinedbased on a speed of propagation of a bonding wave during the bondingprocess. For example, if the bonding wave propagates too fast, this mayresult in a poor quality bonding between two wafers. Thus, past videoinformation of bonding wave propagations having a plurality of differentspeeds (some of which may be labeled normal, others abnormal) may beutilized as training data for training the machine learning circuitry520 or bonding defect detection circuitry 510, and may be stored in thebonding video database 542. Additional types of defects may be detected,for example, defects due to the presence of particles on one of thewafers, defects due to a non-uniform bonding wave propagation speed,defects due to a void in the bonding between two wafers, defects due toa defective pre-treatment or pre-clean process, or any other defects dueto any other cause. A variety of past video information indicative of avariety of different types of defects and their causes may be stored inthe bonding video database 542 and used as training data. Accordingly,the machine learning circuitry 520 or the bonding defect detectioncircuitry 510 may learn to detect or recognize (e.g., throughimplementing video pattern recognition algorithms) the presence orabsence of bonding defects, in real-time, while a bonding process isperformed to bond two wafers to one another.

Additionally, in some embodiments, the machine learning circuitry 520 orthe bonding defect detection circuitry 510 may be trained to detect orrecognize defects in bonding processes performed between wafers having aplurality of different types of bonding interfaces (e.g., materials atthe bonding surface between two wafers being bonded to one another). Forexample, the machine learning circuitry 520 or the bonding defectdetection circuitry 510 may be trained based on past video informationrepresenting bonding defects (or absence thereof) during bondingprocesses of wafers having as a bonding surface any of: a thermaloxidation layer, a poly-silicon layer, silicon nitride, silicon oxide,or any other material.

The past video information used for training the machine learningcircuitry 520 or the bonding defect detection circuitry 510 may includevideo information representing bonding between semiconductor waferswhich are both patterned wafers, both unpatterned wafers (e.g., dummywafers), or one patterned wafer and one unpatterned wafer, for example,as shown and described with respect to FIGS. 3A to 3C. Thus, in someembodiments, the machine learning circuitry 520 or the bonding defectdetection circuitry 510 may be trained to recognize defects in bondingprocesses performed between wafers of a plurality of different types.For example, the past video information may include past videoinformation representing bonding defects (or absence thereof) duringbonding between two unpatterned or dummy wafers, between a dummy waferand a patterned wafer, or between two patterned wafers.

Further, the past video information used for training may include videoinformation representing bonding defects (or absence thereof) duringbonding processes of wafers which were subjected to a variety ofdifferent pre-treatment processes (e.g., Ar plasma, etc.), pre-cleanprocesses (e.g., water clean, etc.), or any other pre-bonding stageprocesses.

In various embodiments, the past video information used for training themachine learning circuitry 520 or the bonding defect detection circuitry510 may include 3-dimensional (3D) video data, such as 3D video datathat is acquired by two or more video cameras in order to construct the3D video data.

In some embodiments, the machine learning circuitry 520 or the bondingdefect detection circuitry 510 may learn to determine an action to betaken in response to determining a defect in the bonding process. Suchactions may include, for example, stopping the current bonding process,performing a rework process (e.g., debonding, cleaning, pre-treating,and bonding again) for the wafers, adjusting a wafer bonding parameter(e.g., increase bonding pressure), and adjusting a pre-stage processperformed by a pre-bonding process tool 550 (e.g., adjusting parametersof a pre-stage film deposition process, a cleaning process, etc.). Thepre-bonding process tool 550 may be any semiconductor processing ormanufacturing tool or apparatus that is used to perform one or moreprocesses on semiconductor wafers before the wafers are bonded in thewafer bonding apparatus 100. The pre-bonding process tool 550 may be, insome embodiments, a process tool configured to perform wafer cleaning,pre-treating of wafers prior to bonding (e.g., a plasma pre-treatingprocess), pre-bonding film deposition (e.g., for depositing one or morefilms on the bonding surface of the wafers, such as a dielectric filmlike SiN, SiO_(x), or the like), or any other wafer processing that maybe performed prior to bonding of the wafers.

In some embodiments, the machine learning circuitry 520 or the bondingdefect detection circuitry 510 may provide or otherwise output anindication of a detected defect during a bonding process which may beutilized to take an action, such as stopping the bonding process,performing a rework process, adjusting parameters, or the like. Forexample, the machine learning circuitry 520 or the bonding defectdetection circuitry 510 may provide a feedback signal, via thecommunications network 501, to the wafer bonding apparatus 100 (e.g., tothe controller 32), and the wafer bonding apparatus 100 may becontrolled, e.g., by the controller 32, to automatically stop thebonding process upon receipt of the control signal that indicates thepresence of a bonding defect. This allows the defective bonding toimmediately be stopped, as opposed to waiting until the wafers arecompletely bonded to one another, or until an entire lot or series ofwafers are bonded.

In some embodiments, the feedback signal may cause the controller 32 toadjust one or more parameters of the wafer bonding apparatus 100, suchas to increase or decrease a bonding pressure applied to bond thesemiconductor wafers to one another, or to increase or decrease apressure within the bonding chamber 10.

In some embodiments, a control or feedback signal may be output, forexample, to a display of a computer device included in orcommunicatively coupled to the wafer bonding apparatus 100 so that auser may be informed that a defect is detected and may therefore stopthe bonding process or perform another action, such as to control oradjust one or more bonding parameters within the wafer bonding apparatus100. The control or feedback signal may further be utilized as part ofan alarm or alert system which provides a perceptible output to a userin order to notify the user that a defect has been detected duringbonding of semiconductor wafers. For example, the perceptible output maybe provided in the form of an auditory alarm, a visual alarm (e.g.,blinking lights or the like provided on a display of a computer deviceor on a panel of the wafer bonding apparatus 100), or the like.

In some embodiments, the machine learning circuitry 520 or the bondingdefect detection circuitry 510 may automatically control parameters of aseparate tool or process (e.g., the pre-bonding process tool 550) inresponse to determining that a defect is present in the wafer bonding.For example, the machine learning circuitry 520 or the bonding defectdetection circuitry 510 may be communicatively coupled to thepre-bonding process tool 550, e.g., via the communications network 501.In such embodiments, the machine learning circuitry 520 or the bondingdefect detection circuitry 510 may determine that the type of defect(e.g., multiple bonding starting points) is due to a defect in apre-treatment or pre-clean process (e.g., pre-layer film process causesabnormal film stress, which causes wafer warpage and thus bonding defectdue to multiple bond starting points), and the machine learningcircuitry 520 or the bonding defect detection circuitry 510 mayautomatically adjust one or more parameters of the pre-treatment orpre-clean process to reduce or eliminate future occurrences of thedetected type of bonding defect. For example, the machine learningcircuitry 520 or the bonding defect detection circuitry 510 mayautomatically output a feedback signal or a control signal to thepre-bonding process tool 550 that causes the pre-bonding process tool550 to adjust one or more parameters (e.g., reduce a thickness of apre-layer film, etc.) of the pre-bonding process in order to reduce oreliminate future occurrences of the bonding defect. In some embodiments,the machine learning circuitry 520 or the bonding defect detectioncircuitry 510 may provide feedback to pre-bonding process tool 550(e.g., a pre-treatment or pre-clean process tool) to indicate asuggested adjustment to be made, or to otherwise optimize processes, inresponse to the detected bonding defect.

FIG. 6 is a flowchart 600 illustrating a semiconductor wafer bondingdefect detection method, in accordance with one or more embodiments. Thesemiconductor wafer bonding defect detection method may be implementedat least in part, for example, by the wafer bonding apparatus 100 shownin and described with respect to FIG. 1 or the wafer bonding monitoringsystem 500 shown in and described with respect to FIG. 5 .

At 602, the method includes bonding a first semiconductor wafer to asecond semiconductor wafer. The first and second semiconductor wafersmay be bonded to one another in the wafer bonding apparatus 100, forexample, within the wafer bonding chamber 10. During the bonding of thefirst and second semiconductor wafers, various parameters of the waferbonding chamber 10 may be set as desired, including, for example, apressure within the wafer bonding chamber 10. Moreover, a bondingpressure may be applied, for example, by the first and second waferchucks 12, 14, in order to press the first and second semiconductorwafers against one another during the bonding.

At 604, the method includes acquiring, by a video camera 18 a, 18 b,video data of the bonding between the first and second semiconductorwafers. The video camera may be an infrared (IR) video camera, and insome embodiments, more than one video camera may be utilized to acquirethe video data. In some embodiments, the acquired video data may beutilized to construct a 3D representation of the bonding of thesemiconductor wafers.

At 606, the method includes determining, based on the acquired videodata, the presence of a bonding defect during the bonding of the firstsemiconductor wafer to the second semiconductor wafer. The defect may bedetermined, for example, by the wafer bonding defect detection circuitry510, which, in some embodiments, may include or otherwise implementmachine learning circuitry 520 to detect or determine the presence ofbonding defects. The determination of the bonding defect may beperformed in real-time, for example, while the bonding of thesemiconductor wafers is being carried out.

Embodiments of the present disclosure provide several advantages. Forexample, embodiments provided herein can detect, in real-time, theoccurrence of bonding defects during bonding of two semiconductorwafers. This facilitates immediate remediation efforts, such as stoppingthe bonding process before further damage occurs, reworking the bondedwafers (e.g., debonding, re-cleaning, re-bonding, etc.), and adjustingbonding or other process parameters to avoid bonding defects in futurewafer bonding processes. This provides significant cost savings in termsof reduced wafer scrap, increased yield, increased reliability, andother factors. Other advantages are described herein and still otherswill be apparent in view of the present disclosure.

According to one embodiment, a wafer bonding system includes a cameraand wafer bonding defect detection circuitry. The camera is configuredto monitor bonding between two semiconductor wafers. The wafer bondingdefect detection circuitry, in use, receives video data from the camera,and detects a bonding defect based on the received video data.

According to another embodiment, a method is provided that includesbonding a first semiconductor wafer to a second semiconductor wafer. Avideo camera acquires video data of the bonding between the first andsecond semiconductor wafers. Wafer bonding defect detection circuitrydetermines the presence of a bonding defect during the bonding based onthe acquired video data.

According to yet another embodiment, a wafer bonding monitoring systemincludes a wafer bonding apparatus that includes a wafer bondingchamber, a camera configured to acquire video information during bondingbetween two semiconductor wafers in the wafer bonding chamber, and alight source configured to emit light toward the two semiconductorwafers during the bonding. The system further includes a wafer bondingvideo database configured to store past video data associated with aplurality of bonding defects, and bonding defect detection circuitry.The bonding defect detection circuitry is configured to receive thevideo information from the camera, and detect a bonding defect based onthe received video data and the past video data stored in the waferbonding video database.

The foregoing outlines features of several embodiments so that thoseskilled in the art may better understand the aspects of the presentdisclosure. Those skilled in the art should appreciate that they mayreadily use the present disclosure as a basis for designing or modifyingother processes and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein.Those skilled in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions, andalterations herein without departing from the spirit and scope of thepresent disclosure.

The various embodiments described above can be combined to providefurther embodiments. These and other changes can be made to theembodiments in light of the above-detailed description. In general, inthe following claims, the terms used should not be construed to limitthe claims to the specific embodiments disclosed in the specificationand the claims, but should be construed to include all possibleembodiments along with the full scope of equivalents to which suchclaims are entitled. Accordingly, the claims are not limited by thedisclosure.

1. A wafer bonding system, comprising: a camera configured to monitor abonding wave between two semiconductor wafers by acquiring video datausing light in the infrared or ultraviolet spectrum during bondingbetween the two semiconductor wafers; a wafer bonding video databasestoring past video data associated with bonding waves during bondingbetween two semiconductor wafers and with a plurality of bondingdefects; and wafer bonding defect detection circuitry, which in use:receives the video data from the camera acquired during the bondingbetween the two semiconductor wafers; and detects a bonding defect,between the two semiconductor wafers, based on the received video dataand the past video data stored in the wafer bonding video database. 2.The system of claim 1 wherein the wafer bonding defect detectioncircuitry, in use: detects the bonding defect by implementing a videopattern recognition process.
 3. The system of claim 1 wherein the waferbonding defect detection circuitry, in use, determines an action to betaken in response to detecting the bonding defect, the action to betaken including at least one of: stopping a current bonding process,performing a rework process on wafers undergoing bonding, adjusting aparameter of the current bonding process, or adjusting a parameter of apre-bonding process.
 4. The system of claim 1, further comprising alight source configured to emit infrared (IR) light or ultraviolet (UV)light toward a bonding interface of the two semiconductor wafers.
 5. Thesystem of claim 4 wherein the light source and the camera are disposedon a same side of the two semiconductor wafers, and the camera isconfigured to receive light that is reflected by at least one of the twosemiconductor wafers.
 6. The system of claim 4 wherein the twosemiconductor wafers are disposed between the camera and the lightsource, and the camera is configured to receive light that istransmitted through the two semiconductor wafers.
 7. A wafer bondingmonitoring system, comprising: a wafer bonding apparatus, including: awafer bonding chamber; a plurality of cameras each configured to monitora bonding wave propagation speed by acquiring video information duringbonding between two semiconductor wafers in the wafer bonding chamber,each of the plurality of cameras positioned at a different position,orientation or angle with respect to the two semiconductor wafers in thewafer bonding chamber; and a light source configured to emit lighttoward the two semiconductor wafers during the bonding; a wafer bondingvideo database configured to store past video data associated withbonding wave propagation speed during bonding between two semiconductorwafers and associated with a plurality of bonding defects; and bondingdefect detection circuitry configured to: receive the video informationfrom each of the plurality of cameras during the bonding; generating a3D image of the bonding of the two semiconductor wafers; and detect abonding defect based on the received video information acquired whilemonitoring the bonding wave propagation speed, and the past video dataassociated with bonding wave propagation speed during bonding betweentwo semiconductor wafers and associated with a plurality of bondingdefects stored in the wafer bonding video database.
 8. The system ofclaim 7 wherein the bonding defect detection circuitry is furtherconfigured to stop the bonding between the two semiconductor wafers inresponse to detecting the bonding defect.
 9. The system of claim 7wherein the bonding defect detection circuitry is further configured toadjust a pressure in the wafer bonding chamber in response to detectingthe bonding defect.
 10. The system of claim 7 wherein the bonding defectdetection circuitry is further configured to adjust a bonding pressureapplied to bond the two semiconductor wafers to one another in responseto detecting the bonding defect.
 11. The system of claim 7, wherein thebonding wave propagation speed is a non-uniform bonding wave propagationspeed.
 12. A wafer bonding monitoring system, comprising: a waferbonding apparatus including: a wafer bonding chamber; a cameraconfigured to monitor a bonding wave propagation speed by acquiringvideo information during bonding between two semiconductor wafers in thewafer bonding chamber, the camera communicatively coupled to a bondingdefect circuitry; and a light source configured to emit light toward thetwo semiconductor wafers during the bonding; a wafer bonding videodatabase communicatively coupled to the bonding defect circuitry andconfigured to store past video data associated with bonding wavepropagation speed during bonding between two semiconductor wafers andassociated with a plurality of bonding defects; and the bonding defectdetection circuitry configured to: receive the video information fromthe camera during the bonding; and after the bonding, detect a bondingdefect based on the received video information acquired while monitoringthe bonding wave propagation speed, and the past video data associatedwith bonding wave propagation speed during bonding between twosemiconductor wafers and associated with a plurality of bonding defectsstored in the wafer bonding video database.
 13. The wafer bondingmonitoring system of claim 12 wherein the wafer bonding defect detectioncircuitry, in use: detects the bonding defect by implementing a videopattern recognition process.
 14. The wafer bonding monitoring system ofclaim 12 wherein the wafer bonding defect detection circuitry, in use,determines an action to be taken in response to detecting the bondingdefect, the action to be taken including at least one of: stopping acurrent bonding process, performing a rework process on wafersundergoing bonding, adjusting a parameter of the current bondingprocess, or adjusting a parameter of a pre-bonding process.
 15. Thewafer bonding monitoring system of claim 12 further comprising a lightsource configured to emit infrared (IR) light toward a bonding interfaceof the two semiconductor wafers.
 16. The wafer bonding monitoring systemof claim 15 wherein the light source and the camera are disposed on asame side of the two semiconductor wafers, and the camera is configuredto receive light that is reflected by at least one of the twosemiconductor wafers.
 17. The wafer bonding monitoring system of claim15 wherein the two semiconductor wafers are disposed between the cameraand the light source, and the camera is configured to receive light thatis transmitted through the two semiconductor wafers.
 18. The waferbonding monitoring system of claim 12 wherein the bonding defectdetection circuitry is further configured to adjust a pressure in thewafer bonding chamber in response to detecting the bonding defect. 19.The wafer bonding monitoring system of claim 12 wherein the bondingdefect detection circuitry is further configured to adjust a bondingpressure applied to bond the two semiconductor wafers to one another inresponse to detecting the bonding defect.
 20. The wafer bondingmonitoring system of claim 14 wherein the bonding defect detectioncircuitry is further configured to stop the bonding between the twosemiconductor wafers in response to detecting the bonding defect.